Hardware acceleration of deep learning using analog non-volatile memory (NVM) requires large arrays with high device yield, high accuracy Multiply-ACcumulate (MAC) operations, and routing frameworks for implementing arbitrary deep neural network (DNN) topologies. In this article, we present a 14-nm test-chip for Analog AI inference-it contains multiple arrays of phase change memory (PCM)devices, each array capable of storing 512 × 512 unique DNN weights and executing massively parallel MAC operations at the location of the data. DNN excitations are transported across the chip using a duration representation on a parallel and reconfigurable 2-D mesh. To accurately transfer inference models to the chip, we describe a closed-loop tuning (CLT) algorithm that programs the four PCM conductances in each weight, achieving <3% average weighterror. A row-wise programming scheme and associated circuitry allow us to execute CLT on up to 512 weights concurrently. We show that the test chip can achieve near-software-equivalent accuracy on two different DNNs. We demonstrate tile-to-tile transport with a fully-on-chip two-layer network for MNIST (accuracy degradation ∼0.6%)
This paper presents a new type of wireless networking applications in data centers using steered-beam mmWave links. By taking advantage of clean LOS channels on top of server racks, robust wireless packet-switching network can be built. The transmission latency can be reduced by flexibly bridging adjacent rows of racks wirelessly without using long cables and multiple switches. Eliminating cables and switches also reduces equipment costs as well as server installation and reconfiguration costs. Security can be physically enhanced with controlled directivity and negligible wall penetration. The aggregate data transmission BW per given volume is expected to scale as the fourth power of carrier frequency. The paper also deals with the architecture of such network configurations and a preliminary demonstration system.
Ultra-high resolution monitors differ from conventional monitors significantly in the display interface to be supported because conventional display interfaces and graphics subsystems cannot provide enough performance to fully exploit ultra-high resolution monitors. The T221 22.2" 9.2M-pixel 204-DPI TFT-LCD monitor can support various display interfaces to support various graphics subsystems/applications, from a PC cluster to a single PC with a commodity graphics card. This paper compares the architecture of the T221 monitor with its predecessor and discusses possible future trends based on our development experiences.
This paper presents a high-speed multimedia content-downloading system using 60GHz millimeter wave (mmWave) technology. For a cost-effective solution, the system makes use of conventional 802.11 for the interactive controls between an access point and smart mobile devices, handling such services as access point discovery, authentication, mmWave link establishment, beam steering, and lost packet recovery. The high-speed one-way mmWave link is dedicated to the transfer of content data. The smart mobile device only has an mmWave receiver so that it can keep its power dissipation and hardware costs low. The content data is downloaded from an access point to such a smart mobile device via an external storage component of the mobile device. To use the high bandwidth of the mmWave, the mmWave receiver writes directly to the external storage as a wireless attached memory, which is accessed by the smart mobile device via its SD interface. We built a proof-of-concept prototype with a smartphone receiving data from an mmWave system, and evaluated the performance.
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